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coda_insert_rows

Insert or upsert rows into a Coda table using column names. Supports up to 500 rows per call with async mutation tracking.

Instructions

Insert one or more rows into a Coda table, with optional upsert.

Inserts up to 500 rows per call. Use column names (not IDs) in the cells. With key_columns, matching rows are updated (upsert) instead of inserted — this is the only way to bulk-update rows. Returns a requestId for async mutation tracking via coda_get_mutation_status. The operation is async (202).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doc_idYesThe doc ID containing the table
table_id_or_nameYesTable ID or name to insert rows into
rowsYesList of rows to insert. Each row is a dict with 'cells' key containing a list of {column: name, value: val} objects. Example: [{'cells': [{'column': 'Name', 'value': 'Alice'}]}]
key_columnsNoColumn names to use as upsert keys. If a row matches on these columns, it is updated instead of inserted. Omit for pure insert.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses async operation (202), returns requestId for status tracking, and upsert behavior. Annotations indicate write and open world, and description adds specific behavioral details without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences, each essential: purpose and upsert variant, limit and column name guidance, async behavior and return value. Efficiently front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers main use case, limits, async nature, and return value. Output schema exists, so no need to detail return shape. No significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with good descriptions; description adds extra guidance about using column names vs IDs, which adds value beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the action 'insert rows' and the resource 'Coda table', with optional upsert. Distinguishes from siblings like coda_update_row by noting 'only way to bulk-update rows'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit context: limit of 500 rows, use column names not IDs, upsert via key_columns. While it doesn't mention alternatives for single-row operations, it implies the distinction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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